Technical Field
The present invention relates to partial discharge signal processing method and apparatus. Partial discharge processing is particularly used for analysing partial discharges in electrical components and systems, such as: medium or high voltage cables, cable joints, overhead line insulators, medium and high voltage switchboard boxes, high and extra-high voltage cables using GIS (Gas Insulated Switchgear).
Description of the Related Art
The term “partial discharges” is intended to indicate an undesired recombination of electric charges occurring in the dielectric (insulating) material of electric components, in the presence of defects of various types, eventually leading to dielectric destruction. Here, a pulse current is generated in portions of dielectric material and causes an electromagnetic wave to propagate through the power or ground cables of the relevant electric system, and radiating through the various surrounding media (dielectric material, metals, air, etc.).
When performing a partial discharge measurement a large number of pulse signals are acquired and processed. Modern instrumentation allows digitizing pulse signals at a very high sampling frequency, so the entire pulse waveforms can be acquired and processed. An operation performed during the measurement process is the selection of specific pulses according to predetermined discrimination criteria. As an example, possible discrimination criteria are: discharge signals acquisition, discharge signals noise filtering, discharge signals classification.
The discharge signals acquisition involves the selection of only some waveform (having a level higher than a specified threshold) among the detected ones. Discharge signals noise filtering involves the selection of actual partial discharge pulses and rejecting noise. Discharge signals classification involves selecting pulses according to their specific characteristics and grouping the most similar into different classes.
Discharge signals acquisition can be based on frequency filtering and level thresholding, implemented by analog circuits. Discharge signals noise filtering and classification are instead performed by selection methods generally based on waveform feature extraction. These algorithms usually work by extracting a small set of parameters (features) from each pulse waveform, and comparing them against specific thresholds, so trying to estimate if each pulse falls within a specific class. The effectiveness of these algorithms is critically depending on the specific feature set chosen.
Selection method employing neural networks are known. Document JP02-296162 describes a method for separating and detecting external noise and partial discharge signals. According to this method a shielding layer of a cable is cut so obtaining two opposite cut ends. A pulse shaped voltage waveform generated at one of the cutting end is compared to another pulse shaped voltage generated at the other cut end by means of a neural network. Both the two pulse shaped voltage waveforms are inputted at an input layer of the neural network. This comparison allows distinguishing partial discharge signals (for which similar pulses are generated at both cut ends) from noise signals.
Document JP02-296161 discloses a method for detecting partial discharge position by making a neural network learn different waveforms formed by the partial discharge. An electrical unit where the partial discharge is generated is detected based on the result of the learning and the wave of the newly generated partial discharge.
Document JP08-338856 illustrates a method for deciding whether a partial discharge is present or not. The method includes the steps of: teaching a neural network; detecting partial discharge and noise signals from a designated point of a power cable a plurality of times; feeding a detected signal to the neural network and determining an evaluation value; averaging a plurality of evaluation values and comparing with a threshold value.